Systems and methods for providing recommendations are often unable to quantify similarities and differences between objects in a way that would allow for recommendations based on the similarity of objects (e.g., “If you like X, you will also like Y”).
Systems and methods for providing recommendations are often also unable to provide enough variability between the recommended object and the initial input object. For example, in conventional recommendation systems, a person who indicates that they may like a “Toyota Corolla” may be presented with a “Toyota Corolla Sport” which is too similar to be considered a distinct option from the initial input. Additionally, many vehicle recommendation engines are unable to provide recommendations that incorporate variety. For example, a person may be asked to enter information about a make and model in order to view similar vehicles (all having the same make and model but different luxury specifications) but not be presented with vehicles of a different brand.
Additionally, some conventional systems and methods for providing recommendations, may present users with recommendations that are not particularly relevant or accurately responsive to their needs. For that reason, users may have to spend immense amounts of time to iteratively screen thru a list of recommendations and traverse a user interface. For example, it is estimated that a user may spend approximately 8 hours scrolling thru recommendations and/or browsing a website or mobile application for viewing vehicles on a conventional recommendation engine.
Moreover, conventional recommendation systems are based primarily on direct user input, which requires a user to have extensive knowledge of the field. In particular, vehicle recommendations often require an individual to enter their preferences directly. Preferences may include make, model, mileage, location, optional features, and condition. However, users may not always be able to recall or have knowledge of the preferences they would like. Additionally, the users may be better suited for a style or option for an object that is different than the preference they self-select.